ECE/CS/ME 539 lecture Notes

ECE/CS/ME 539 Introduction to Artificial Neural Networks and Fuzzy Systems

Tentative Course Outline

Fall 2008 Semester

Dates	Topic and Power Point Files	References, Assignment
9/3	Lec 1. Introduction Lec 2. Applications of Artificial Neural Network: speech, image, information retrieval, time series prediction, etc.	[HuHwang] Chap. 12
9/5	Lec 3. Neuron model	[Haykin] Chap. 1
9/8, 10	Lec 4. Learning: Function approximation, bias versus variance	[Haykin] Sec. 2.13
9/12, 15	Lec 5. Learning: Error correcting learning and LMS algorithm	[Haykin] Sec. 3.4 - 3.7
9/17	Lec 6. Learning: Hebbian learning and principal component analysis	[Haykin] Sec. 2.4, 8.2 - 8.8
9/19, 22	Lec 7. Learning: Perceptron	[Haykin] Sec. 3.8 - 3.9
9/24, 26	Lec 8. Pattern Classification (1): Bayesian, ML, Non-parametric method
9/29	Lec 9. Pattern Classification: Implementation
10/1, 3	Lec 10. regression, approximation, time series models
10/6, 8	Lec 11. Feed-forward multi-layer perceptron (MLP)	[Haykin] Sec. 4.2
10/10	Lec 12. Review: Nonlinear optimization	[Haykin] Sec. 3.3
10/13, 15	Lec 13. Back-propagation training of MLP	[Haykin] Sec. 4.3 - 4.5
10/17, 20	Lec 14. MLP implementation issues	[Haykin] Sec. 4.6 - 4.10
10/22	Lec 15. Radial Basis Network (I): Interpolation	[haykin] Sec. 5.2 - 5.6
10/24, 27	Lec 16. Radial Basis Network (II): Approximation	[haykin] Sec. 5.7 - 5.10
10/29, 31	Lec 17. Support Vector Machine (I): Linear Seperability	[haykin] Sec. 6.2
	Monday 11/3, 7:15-8:45 PM, Midterm Exam (Evening),	2535 Engr. Hall
11/5	Lec 18. Support Vector Machine (II): Non-separable cases	[haykin] Sec. 6.3
11/7	Lec 19. Support Vector Machine (III): Kernel formulation
11/10	Lec 20. Clustering: Competitive Learning	[haykin] Sec. 9.2 - 9.6
11/12	Lec 20. Clustering: Probability Density Estimation, Parsen Window
11/14	Lec 20. Mixture of Gaussian, EM algorithm, Kmeans algorithm
11/17	Lec 21. Clustering: Self-Organizing Map, Learning Vector Quantization
11/19	Lec 22. Clustering: Implementation Issues,
11/21	Lec 23. Fuzzy Logic: Fuzzy set theory	[JSM] Chap. 2
11/24	Lec 24. Fuzzy Logic: Fuzzy Reasoning	[JSM] Chap. 3
11/26	Lec 25.Fuzzy Logic: Fuzzy Inferencing	[JSM] Chap. 4
12/1	Lec 26. Fuzzy Logic Control (I)	[JSM] Sec. 17.1 - 17.3
12/3	Lec 27. Fuzzy Logic Control (II)	[JSM] Sec. 17.4 - 17.6
12/5	Lec 28. Fuzzy Logic Control (III): Applications
12/8	Lec 29. Bayesian Network
12/10	Lec 30. Committee Machine	[Haykin] Chap. 7
12/12	Lec 31. Stochastic Optimization: Simulated annealing, genetic algorithm	[JSM] Chap. 7
12/12	student project presentation, Take home final exam. distributed at 4PM, Dec. 12.
	Noon, Friday, 12/19/2008, Final exam and project report due

References

[HuHwang] Y. H. Hu and J.-N. Hwang, "Handbook of Neural Network Signal Processing," CRC Press, 2002
[Haykin] S. Haykin, "Neural Network: A Comprehensive Foundation,", (2nd Ed.), Prentice Hall, 1999 (Text book)
[JSM] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-Fuzzy and Soft computing," Prentice Hall, 1997 (Reference book)

Last Modified: November 5, 2008

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